Non-ignorable missing covariate data in survival analysis: A case-study of an International Breast Cancer Study Group trial

Journal Article (Journal Article)

Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.

Full Text

Duke Authors

Cited Authors

  • Herring, AH; Ibrahim, JG; Lipsitz, SR

Published Date

  • May 20, 2004

Published In

Volume / Issue

  • 53 / 2

Start / End Page

  • 293 - 310

International Standard Serial Number (ISSN)

  • 0035-9254

Digital Object Identifier (DOI)

  • 10.1046/j.1467-9876.2003.05168.x

Citation Source

  • Scopus